{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.使用scipy读取文件，保存为dict格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io as sio\n",
    "import h5py\n",
    "f=sio.loadmat(\"./pascal_context/features/deeplab_largeFOV/val/fc8/2007_000033_blob_0.mat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.使用dict的keys关键字，查看字典中的key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LabelMap', '__version__', '__header__', '__globals__']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=sio.loadmat(\"/home/dl/DL_dataset/VOCdevkit/trainval/2008_000023.mat\")\n",
    "t.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__version__', 'data', '__header__', '__globals__']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "test=sio.loadmat(\"/home/dl/deeplab/exper/voc12/features/deeplab_largeFOV/val/fc8/2007_000033.mat\")\n",
    "test.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 8.91384125],\n",
       "         [ 1.3823266 ],\n",
       "         [-0.32057345],\n",
       "         ..., \n",
       "         [ 0.78105485],\n",
       "         [-0.37161291],\n",
       "         [-0.04199338]],\n",
       "\n",
       "        [[ 8.94016552],\n",
       "         [ 1.41513276],\n",
       "         [-0.3097305 ],\n",
       "         ..., \n",
       "         [ 0.78504121],\n",
       "         [-0.35622093],\n",
       "         [-0.03176174]],\n",
       "\n",
       "        [[ 8.96648979],\n",
       "         [ 1.44793904],\n",
       "         [-0.29888755],\n",
       "         ..., \n",
       "         [ 0.78902751],\n",
       "         [-0.34082896],\n",
       "         [-0.02153009]],\n",
       "\n",
       "        ..., \n",
       "        [[ 7.43813753],\n",
       "         [ 0.06125358],\n",
       "         [-1.14916027],\n",
       "         ..., \n",
       "         [ 1.35218096],\n",
       "         [-0.18225437],\n",
       "         [ 1.52334714]],\n",
       "\n",
       "        [[ 7.45029497],\n",
       "         [ 0.05824194],\n",
       "         [-1.18775678],\n",
       "         ..., \n",
       "         [ 1.36530244],\n",
       "         [-0.2221432 ],\n",
       "         [ 1.54795837]],\n",
       "\n",
       "        [[ 7.46245289],\n",
       "         [ 0.05523029],\n",
       "         [-1.22635329],\n",
       "         ..., \n",
       "         [ 1.37842393],\n",
       "         [-0.26203203],\n",
       "         [ 1.57256961]]],\n",
       "\n",
       "\n",
       "       [[[ 8.9234314 ],\n",
       "         [ 1.38217068],\n",
       "         [-0.31871715],\n",
       "         ..., \n",
       "         [ 0.78806388],\n",
       "         [-0.36657128],\n",
       "         [-0.04765262]],\n",
       "\n",
       "        [[ 8.94991684],\n",
       "         [ 1.41518247],\n",
       "         [-0.30766159],\n",
       "         ..., \n",
       "         [ 0.79141116],\n",
       "         [-0.35104182],\n",
       "         [-0.03715125]],\n",
       "\n",
       "        [[ 8.97640228],\n",
       "         [ 1.44819438],\n",
       "         [-0.29660603],\n",
       "         ..., \n",
       "         [ 0.79475844],\n",
       "         [-0.3355124 ],\n",
       "         [-0.02664989]],\n",
       "\n",
       "        ..., \n",
       "        [[ 7.40180016],\n",
       "         [ 0.07613266],\n",
       "         [-1.12707055],\n",
       "         ..., \n",
       "         [ 1.34359682],\n",
       "         [-0.16667186],\n",
       "         [ 1.4956485 ]],\n",
       "\n",
       "        [[ 7.41400957],\n",
       "         [ 0.07405037],\n",
       "         [-1.16464138],\n",
       "         ..., \n",
       "         [ 1.35555935],\n",
       "         [-0.2051492 ],\n",
       "         [ 1.52060175]],\n",
       "\n",
       "        [[ 7.42621899],\n",
       "         [ 0.07196809],\n",
       "         [-1.20221221],\n",
       "         ..., \n",
       "         [ 1.36752176],\n",
       "         [-0.24362655],\n",
       "         [ 1.545555  ]]],\n",
       "\n",
       "\n",
       "       [[[ 8.9330225 ],\n",
       "         [ 1.38201487],\n",
       "         [-0.31686085],\n",
       "         ..., \n",
       "         [ 0.79507297],\n",
       "         [-0.36152965],\n",
       "         [-0.05331185]],\n",
       "\n",
       "        [[ 8.95966911],\n",
       "         [ 1.4152323 ],\n",
       "         [-0.30559272],\n",
       "         ..., \n",
       "         [ 0.79778117],\n",
       "         [-0.34586275],\n",
       "         [-0.04254077]],\n",
       "\n",
       "        [[ 8.98631477],\n",
       "         [ 1.44844973],\n",
       "         [-0.29432455],\n",
       "         ..., \n",
       "         [ 0.80048937],\n",
       "         [-0.33019581],\n",
       "         [-0.03176969]],\n",
       "\n",
       "        ..., \n",
       "        [[ 7.36546421],\n",
       "         [ 0.09101173],\n",
       "         [-1.10498095],\n",
       "         ..., \n",
       "         [ 1.33501267],\n",
       "         [-0.15108934],\n",
       "         [ 1.46794987]],\n",
       "\n",
       "        [[ 7.37772465],\n",
       "         [ 0.08985882],\n",
       "         [-1.14152598],\n",
       "         ..., \n",
       "         [ 1.34581602],\n",
       "         [-0.1881552 ],\n",
       "         [ 1.49324512]],\n",
       "\n",
       "        [[ 7.38998508],\n",
       "         [ 0.0887059 ],\n",
       "         [-1.17807102],\n",
       "         ..., \n",
       "         [ 1.35661948],\n",
       "         [-0.22522107],\n",
       "         [ 1.51854038]]],\n",
       "\n",
       "\n",
       "       ..., \n",
       "       [[[ 8.30411911],\n",
       "         [ 1.12645459],\n",
       "         [-1.37936461],\n",
       "         ..., \n",
       "         [ 0.135711  ],\n",
       "         [ 0.65620011],\n",
       "         [ 2.42010951]],\n",
       "\n",
       "        [[ 8.29532909],\n",
       "         [ 1.12747049],\n",
       "         [-1.38180709],\n",
       "         ..., \n",
       "         [ 0.12408188],\n",
       "         [ 0.66541392],\n",
       "         [ 2.44043159]],\n",
       "\n",
       "        [[ 8.28653908],\n",
       "         [ 1.12848639],\n",
       "         [-1.38424969],\n",
       "         ..., \n",
       "         [ 0.11245276],\n",
       "         [ 0.67462778],\n",
       "         [ 2.46075392]],\n",
       "\n",
       "        ..., \n",
       "        [[ 6.99892902],\n",
       "         [-1.07713509],\n",
       "         [-1.94047928],\n",
       "         ..., \n",
       "         [ 2.86829352],\n",
       "         [-1.74740458],\n",
       "         [ 1.39230633]],\n",
       "\n",
       "        [[ 7.01716805],\n",
       "         [-1.07156694],\n",
       "         [-1.9460367 ],\n",
       "         ..., \n",
       "         [ 2.85312414],\n",
       "         [-1.7542336 ],\n",
       "         [ 1.3887639 ]],\n",
       "\n",
       "        [[ 7.03540707],\n",
       "         [-1.06599867],\n",
       "         [-1.95159411],\n",
       "         ..., \n",
       "         [ 2.837955  ],\n",
       "         [-1.76106274],\n",
       "         [ 1.38522148]]],\n",
       "\n",
       "\n",
       "       [[[ 8.29134464],\n",
       "         [ 1.14344442],\n",
       "         [-1.37503695],\n",
       "         ..., \n",
       "         [ 0.13523564],\n",
       "         [ 0.65910006],\n",
       "         [ 2.40658975]],\n",
       "\n",
       "        [[ 8.28259182],\n",
       "         [ 1.14422977],\n",
       "         [-1.37732315],\n",
       "         ..., \n",
       "         [ 0.12345587],\n",
       "         [ 0.6680426 ],\n",
       "         [ 2.4262414 ]],\n",
       "\n",
       "        [[ 8.27383804],\n",
       "         [ 1.14501512],\n",
       "         [-1.37960947],\n",
       "         ..., \n",
       "         [ 0.1116761 ],\n",
       "         [ 0.67698514],\n",
       "         [ 2.44589329]],\n",
       "\n",
       "        ..., \n",
       "        [[ 7.0197897 ],\n",
       "         [-1.0820049 ],\n",
       "         [-1.94536638],\n",
       "         ..., \n",
       "         [ 2.87202024],\n",
       "         [-1.75621307],\n",
       "         [ 1.39777422]],\n",
       "\n",
       "        [[ 7.03800106],\n",
       "         [-1.07642651],\n",
       "         [-1.95101964],\n",
       "         ..., \n",
       "         [ 2.85683036],\n",
       "         [-1.76322591],\n",
       "         [ 1.39404082]],\n",
       "\n",
       "        [[ 7.0562129 ],\n",
       "         [-1.07084811],\n",
       "         [-1.95667291],\n",
       "         ..., \n",
       "         [ 2.84164047],\n",
       "         [-1.77023864],\n",
       "         [ 1.39030755]]],\n",
       "\n",
       "\n",
       "       [[[ 8.27857018],\n",
       "         [ 1.16043437],\n",
       "         [-1.3707093 ],\n",
       "         ..., \n",
       "         [ 0.13476026],\n",
       "         [ 0.66199994],\n",
       "         [ 2.39306998]],\n",
       "\n",
       "        [[ 8.26985359],\n",
       "         [ 1.16098917],\n",
       "         [-1.37283921],\n",
       "         ..., \n",
       "         [ 0.12282984],\n",
       "         [ 0.67067116],\n",
       "         [ 2.4120512 ]],\n",
       "\n",
       "        [[ 8.26113701],\n",
       "         [ 1.16154397],\n",
       "         [-1.37496924],\n",
       "         ..., \n",
       "         [ 0.11089942],\n",
       "         [ 0.67934245],\n",
       "         [ 2.43103266]],\n",
       "\n",
       "        ..., \n",
       "        [[ 7.04064941],\n",
       "         [-1.08687472],\n",
       "         [-1.95025325],\n",
       "         ..., \n",
       "         [ 2.87574697],\n",
       "         [-1.7650218 ],\n",
       "         [ 1.40324199]],\n",
       "\n",
       "        [[ 7.05883408],\n",
       "         [-1.08128619],\n",
       "         [-1.95600247],\n",
       "         ..., \n",
       "         [ 2.86053634],\n",
       "         [-1.77221823],\n",
       "         [ 1.39931774]],\n",
       "\n",
       "        [[ 7.07701874],\n",
       "         [-1.07569766],\n",
       "         [-1.96175182],\n",
       "         ..., \n",
       "         [ 2.84532571],\n",
       "         [-1.77941453],\n",
       "         [ 1.39539361]]]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text1=test['data']\n",
    "text1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.使用plot显示mat文件，单通道的文件使用plt可以显示成彩色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALoAAAEACAYAAAAEKGxWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efcf0c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.读取一个图片的mat文件，并保存成彩色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import PIL.Image as Image\n",
    "from matplotlib import pyplot  \n",
    "\n",
    "mat_file=sio.loadmat(\"/home/dl/DL_dataset/VOCdevkit/trainval/2008_000023.mat\")\n",
    "mat_file=np.array(mat_file['LabelMap'])\n",
    "labels_path=\"/home/dl/DL_dataset\"\n",
    "dst_path=os.path.join(labels_path,'2008_0000230'+'.png')\n",
    "\n",
    "\n",
    "#pyplot.imsave(dst_path,mat_file)  \n",
    "mat_file=mat_file.astype(np.uint8)\n",
    "label_img=Image.fromarray(mat_file.reshape(mat_file.shape[0],mat_file.shape[1]))\n",
    "label_img.save(dst_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "path='/home/dl/DL_dataset/VOCdevkit/trainval'\n",
    "files=os.listdir(path)\n",
    "labels_path=os.path.join(path,'label2')\n",
    "mat_idx=afile[:afile.find('.mat')]\n",
    "mat_file=sio(file_path)\n",
    "for afile in files:\n",
    "    file_path=os.path.join(path,afile)\n",
    "    if os.path.isfile(file_path):\n",
    "        if os.path.getsize(file_path)==0:\n",
    "            continue\n",
    "        mat_idx=afile[:afile.find('.mat')]\n",
    "        mat_file=sio(file_path)\n",
    "        mat_file=np.array(mat_file['LabelMap'])\n",
    "       # print mat_file.keys()\n",
    "        mat_file=mat_file*255\n",
    "        mat_file=mat_file.astype(np.uint8)\n",
    "        #print type(mat_file)\n",
    "        #print mat_file.shape()\n",
    "        label_img=Image.fromarray(mat_file.reshape(mat_file.shape[0],mat_file.shape[1]))\n",
    "        #label_img.putpalette(palette)\n",
    "        #label_img=label_img.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "#       label_img.show()\n",
    "        dst_path=os.path.join(labels_path,mat_idx+'.png')\n",
    "        label_img.save(dst_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取文件夹中的文件名，制作train.txt文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "path='/home/dl/DL_dataset/VOCdevkit/trainval'\n",
    "files=os.listdir(path)\n",
    "f=open(\"/home/dl/DL_dataset/VOCdevkit/train_aug.txt\",\"w\")\n",
    "#labels_path=os.path.join(path,'label2')\n",
    "#mat_file=sio(file_path)\n",
    "for afile in files:\n",
    "    file_path=os.path.join(path,afile)\n",
    "    if os.path.isfile(file_path):\n",
    "        if os.path.getsize(file_path)==0:\n",
    "            continue\n",
    "        #print \"read it!\"\n",
    "        mat_idx=afile[:afile.find('.mat')]\n",
    "        f.write(\"/JPEGImages/\"+mat_idx+\".jpg \")\n",
    "        f.write(\"/PASCALContexts/\"+mat_idx+\".png\"+\"\\n\")\n",
    "        #dst_path=os.path.join(labels_path,mat_idx+'.png')\n",
    "        #label_img.save(dst_path)\n",
    "f.close()\n",
    "print \"finished!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEACAYAAAD2sW7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efcfc5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAADeCAYAAAAtk/tvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDQpoa7X44QhhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd1b1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+X+ON1tZfj6oqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd2bde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGcVJREFUeJzt3Xl4VNXhxvHvZCEhIUASlgAh7IRFFBHFgKCiFlm0FaEU\nWar4K7YuuFC31lqxdtPS1qV1FxVbLSKIyqIotigiIKAigmxhExBC2CFkm98fAzGQPXPnnjtz3s/z\n5CGz3fs+kLycOXPvuT6/34+IiNgjynQAERFxl4pfRMQyKn4REcuo+EVELKPiFxGxjIpfRMQyKn4R\nEcuo+EVELKPiFxGxjIpfRMQyKn4REcvEmNrxAz6f5xYJmsRvTUcwaiTvMYjFpmOEtYMkcBN3mo7h\nWVOZZDqCcX87cyYrmvwoqG345+ML5vUa8Z+wm8amI4hEvEmMMx3BuK9TLjEdQcV/0htcbTqCRICn\nuMp0BJEqqfhFHLSK9qYjiFRJxS8iYhkVv5Q4SKLpCGFtCoNNRxCpFhW/lJhNH9MRRMQFKn4REcuo\n+EVELKPil1Psp57pCCISYip+OcUdTDAdQURCTMUvZeSbW8kjbBUDBfp7kzCh4pdTFBDLnxhjOkbY\n2U8SH9HddAyRalHxSxnrySCPWNMxRCREVPxSrp9xL1/R1nQMEQkBFb9UwMdaWuG5tbNFJGgqfqnQ\nLPoxnYtNxxARh6n4pVJv0c90BBFxmIpfqvR7fkqhflREIoZ+m6VKa2lNkX5URII2N+N28qLNnx2v\n32aplv/j16yhlekYImGtyBcDvqAul+sIFb9U20o6mo4gIg5Q8YuIWEaLi4irHuXXrKNLyPdzMfMY\nxtSQ70ckHKn4xXEfcvkpt18wsOLnLEYyi5Fl7m/PGvoxv8z9FzPPjVginqDiF8fcxosA7KWJ2SCV\n2EBnNtC5zP2z+AkAf+dalxOJuE/FL7W2kxZsoBPPMNF0lKCd/M9qDHMAGM9kzmAFyewzGUskJFT8\nUmMf05+ZXMMxEjhEQ9NxQuIZJtKQvXTka27hj6bjiDhKxS/VtpOWXM8M8ok3HcUV+0llKX0ZQ1/u\n4j66scJ0JBFH6HBOqbbPOdea0j/d3/gNqznLdAwRR6j4RaqhgDj+pCkfiRAqfhERy6j4RUQso+IX\nEbGMiv+EdLabjiAi4goV/wlX8I7pCCIirlDxi4hYRsUvImIZFb+IiEty41qajgCo+EVEXDM/4xbT\nEQAVv4iIdVT8IiKWUfGLiFhGxS8iYhkVfykJHDEdQUQk5FT8pVzB26YjiIiEnIpfRMQyKn6Rarr4\nxIXYRcKdil+kms7iM9MRRByh4pcaOAgUmw4hIkFS8UsNHEXFLxL+VPwiIpZR8YuIWEbFLyJiGRW/\niIhlVPylpLKXehwyHUNEJKRU/KU0JockFb+IRDgVv4iIC+a1vNV0hBIqfhERF/ynw59NRyih4hcR\nsYyKX0TEMip+ERHLqPhFRCyj4hcRsYyKX0TEMip+ERHLqPhPM4zppiOIiISUiv80KewzHUFEJKRU\n/CIillHxi4hYRsUvImIZFb+IiGVU/CIilokxHUDMqBNVSM+0HTSMy2P20FdL7j9eGE3/18cC8MmO\nDFPxRCSEVPwW+WXPRfTP2AxAXHRhyfelxcUUsWjkFPx+mLe5PQCDZoxyMaWIhJqKP4K1a5jLhusf\nr9VrfT4Y2GYDAP6Jk7jg1etYtCODfTcNJ/kfM52MKSIu0xx/hGrbIJePRkxxbHsfj5zCtV1XOrY9\nEZssaTKMIp93xtneSSJlZCbnEBtdVK3nHimoQ/aB5JLbPdN20KzeYUfzTLn8LQAe6/8kExb8wtFt\ni0SyXQmZ+H3RpmOUUPF71GuDpzOk3ToSYwuq9fzdRxP4cGubktsjOq0OVTQub72c+nWOcDA/MWT7\nEJHQUfF7TIO4PNaNe5wmCUdr9LomCUdDWvaldUjewYFbhtPxhWfI3t+MQr93RjIiUjUVfzmy+ITF\n9HZ8u9G+Yl4YMOuU+2Znd2DaN2eU3H736ldqXPqmrBs3nunr+vDu5nN4btXlpuMYlcgxOpPNGtpU\n/WQRw1T85WhDdtDFnxx/jKTY49yftZDru1X8oejYrl/ynyFvBLUvk4Z1XMSwjot49geP0eqZKew4\nkkphsX0/VnEU0oy9Kn4JC/b9hobY2C5f0Dd9C72bb6NLao7pOK7aMv46Xl/Xhw+2dOfpLwebjiMi\nFVDxO6BpQuDomV2/mGw4iXnDOy5ieMdFPHXZP0h78hUAvjuaYjiViJSm4neACr98u34xGoB/rBzC\n5OVDyT6QZjiRiIBO4ApaYky+6Qied9PZ77Dp/8bxvxF30jDukOk4IXMds01HEKkWFX+Qnh3wtukI\nYaNf+mr23TyCyRc+S2bydtNxRKyl4g+CDz/RvmLTMcLOHT1nsnbceJaNmkDdmDzqxuQR7aveGcoi\nEjzN8QehU0oOP8782nSMsNUzbQNHbx0KwIz1vfkmNx2AX318rcFUIpFPxR+E6CiN9p0ytMMnJd/f\n22saAFsPNqbNcy8APor9enMq4hQVfxBW/fQp0xEiWkb9PRTdcQWH8+P5y2dXl9w/abGuDyASDBW/\neF69Onk80PtfJbfvO//7K4Y98Mkofr9kpIlYImFLxV9L06+YZjqCtWJKTbE9dMFUHrpgapnnLPq2\nM+9sOq/CbazZm8GsjVlV7uue8/5T8n0fviX1KGz8qoaBRTzGWPH/gfvLvf9XPOhyEolEfVqsoU+L\nNRU+XlgcxfGi2DL3f7ClO3ExBVzQIrDSaWLs8VMe9/vh3P7w2mPO5hVxk7HiL6BOufdP4qFy748j\njwtZUO5jGWylBe4dF35W411c3bHiUhHvi4kqJibqeJn7r2y/pNLX+XwQEwujJ0L2Glg0J1QJ7VIn\nDtqeUfXzyrN2ubNZbBA2Uz3Hiec9BpX7WAwFxFD+BUtuZTLxlP0Fr0xbNtGcb9lBixrnFHu06QzN\nW8OyBbB5rek04Wn4jYE/fVGB8q+Nbr0Cf677HL74pPLnSkDYFH9lComlkLJv2wH+zG8qfN1Ayj/r\ntiH7iaLiQzU/H/t0zQJKxIqrCxcMhqgo2KRTOqrUqBmMvsbZbcbVDfzZLSvwBbByIRScGAuu+9zZ\n/dXG9Pblz2SYEhHFX1tzuaLc+6MpxH/iu1P5efHy6SFOJeEoawCBnxmVf6XevurVqp/kgLP7ff/9\nyXcEJ23fBEvmuxLDs3RWTDmKiKGYGMB32lcUn+1qaTSbeJMvCvoMNJ3C+xrVPeb6PuvWO/Wrw5mB\nz2janfH9uwXbWD3ir40nPs9i44EU5gwtewihiFRs6sAZpiOcImsAHD0MOzfD4ndNp3GXRvy1MDc7\nkwkLdIUpKevAzX9kdOcvTMfwpLTEw6YjlJFQLzDyHz0x8GF9fILpRO5Q8dfS4yuzGDxjjOkY4jH1\n4/KZOuhNlo16xnQUTzm36bdkNff2Utx9BsHAUYE/I52KPwhzsjO5dYEFPyVSYz3TdvLywJmmY3hG\n3dgCEmPLP+TaSxLrB0b+oydCWobpNKGj4g/SYyt7c+ZLN/PWxk6mo4jHjOnyJUtGPWs6hie8MOAt\n0xFq7KIfweCxplOEhorfAaty0vjhm6Np/M972Xygoek44iHnpe3g5YEzuLLdN6ajGNOn+VbaNdxn\nOkaNxcRCcuPA6L9lB9NpnKXid1DOsUQumna96RjiMWO6rOLNH77Gz7rZubbAH/p+YDpC0Hr0NZ3A\nWSp+h205mEzak3ebjiEe4/PBMz94h0Ft1pmO4orGdY8woPUG/BMn0S99q+k4QUtKhqt+BvGJppM4\nQ8UfArl5CcxY38V0DPGg2UNfZfyZn5mOEVLPD5jFhyNeZN7V/6r6yWEksT5c9mPTKZzh8/v9Znbs\n+72ZHbto4Yhn6Zu+xXQM8aj2z9/Cxv0ppmM45ty0b1k66jnTMULu6CGY8wrkHa3+a8Zc6mzd+efj\nC+b1GvGHUP/XxzF9XVfTMcSjFgx/ifeHvczN3ZeajhK094e9zILhL5mO4YqEpMCqrOFMSzaEUGFx\nNB9tb8WwjqtNRxEPyqh/kIz6B7mkVTaPXzKXNzdk8vCyPuw+mhg27wQe7P0hv8laaDqG63oPDO/V\nWDXV44KhHVbzxpXurEoo4W/7oSS+3NOUwTO9fVH5KQPe5Noz7F2eYuNqWDyves/VVI+FZqzv6tja\nPvvy4ln+XXNHtiXelJ50iEFtA0fE+CdOokeTHfRossN0rFM82HuB1aUPgemecF3dU1M9Lnl8ZRY7\njyTx+hWvBbWdy9/4KUt3tcQ/8T6HkonXLR8TOPt35vrA2eErdqfx0KcXGsvz/A9mMa6bB65uYljd\nRGjZHjasMp2k5jTV47Kbun/K4/3fwVfFG7XcvHh2HK5fcrvbSxNOeXxw27W8c9UroYgoYWTcvCtZ\nuitwidBjhTFsOhC6zwY6pexhVOdV3Hf+RyHbR7j54hNYtbjy52xP7Mq9WV85ut9gp3pU/AaMyPyS\nqzp8zYjM8n8Yfj7/Slbsbs6yXekVbqN54kH+N+I52ifnhiqmhJm9x+oyf0vbktsjZw+r9bZeHVz2\nSnNXdVhLXHRRrbcZqV6ZXPnjYy4ppsqRXg2p+C32+hWv6oghqdKWgw249PWKlxBPjC3QdaSD8OZz\ncPhAxY+r+EvvWMXvCM31i5h17Ai88VTFj3ux+HVUT5jbfTRCFg8REdeo+MPc6Dm1n8cVETup+MPc\n/C0dWLi9lekYIhJGVPwR4NEVvU1HEJEwouKPADPWd+VQfh3TMUQkTKj4I0SXKbeybl+q6RgiEgZU\n/BFi++EGrFfxi0g1qPgjyJCZY01HEJEwoOKPMFn/Ho+hc/JEJEyo+CPMpzszGDtXx/aLSMVU/BHo\nlTXd6fWvG0zHEBGPUvFHqKW7WjJ27tWmY4hEvLqJMHoidDk38BUOdCGWCDb167P5JrcRS0Zp5UWR\nUOvRL/Bnt/Phi0WwdoXZPJXRiD/CLd3VkmvnDuWl1d1NRxGxQmwd6HkxdOoBqWmm05RPyzJbIspX\nzGuD/8PwTK3fL+KWwgI44Euh0eq9jm5XyzJLtRT7o5iTnWk6hohVYmIhNSYX/1k+/Gf5OD+hius0\nusTgiH+NH2YY2bfNejXbxqfXaM5fxITDRYmkfb2LI8X1gtpOGI/4OwH9ze3eUkt2tmTcvKtMxxCx\nUr3oIxzulmQ6humpnvOBoWYjWGjK6nPI+vd40zFErHVf098Z3b8H5vg18jfh050ZXKeRv4gRv0u7\nnxtSK7lQb4gZnOM/fcdr0Zy/+7KabeWTa54xHUPESr4vate/YTzHf7pOwK+ANqaDWOWrvU1Yvy/F\ndAwRK/2jxY1G9uuh4j9pOCp/9xzKj2dudkfTMUSs9OOG02gUvcf1/Xpoqud0/wK2uJJFwD/xPtMR\nRKxV0ymfCJrqOd0IoK3pENboP22c6Qgi4hIPF38M8BNgAhDcyQ5StQ+3teUvy/qYjiEiLvBw8Z9U\nDxgNaInhUPvn571MRxARF4RB8QOkAJkEjvrJAMyf+RaJsg+msHB7K9MxRKzTMe4bV/fn4Q93K7P3\nxNd0p+I4KjG5kBunbS33sY9fSmbxK8kuJ6q++JgCjt06yXQMEat0WbuaNce7VPv5wX64G6bFX9qL\nwA5nNhWEJu2Ok94tjwkzq3ck0h0ZncjdVifEqWrnhjOX8tRlb5mOIWINHdVTY2OBzkYTNGhWwD3/\n3VTt0ge4+4NNRMd485IET395nukIIhJCETDiP8kPPA4cdnazVWjUOp+/ZK+t9etva9GZ/TtiHUzk\nDB9+tt/wZ5rXc/fvU8RGGvHXmg+4BrdX+xz2h11Bvf6eDzcSG1/sUBrn+PFp5C8SoSKo+AEaEVjz\nZ9CJ790Q3BuXtI75PHvsK4eyOOvBxf35Yk9T0zFEItthaF70rau7jLDiP6k7MB74IYETwbyv95h9\npiOUq/vLt/Da2m6mY4hErk9h4e5+/HvvSNd2GaHFf1JX4C4Cx/83BxLMxqnE+Je30brnUdMxyvXF\nnjTTEUQiUwHgh3ZFmxh57DX8231ckvc+TYuCm0KuSoQXf2nXAmOAIcBZZqNU4IFlGzw58p+9KZPj\nRdGmY4hEnm+B/FPvej/nMhbvzgrp9I9FxQ+QCpwJDCbwLiCJYP8Kpt7cIvhYpYx/eRutenhr5L8q\nJ40tBxqajiESeQrKv7tN0Wa+3ZlOeuE2Yv355T8pCJYV/+luAa4k8JlA7Yx5wvn/lSct30Dv0d4a\n+WdOud10BJHIs67yh7ftyqBrwWrHd2t58QN0IXAU0K8IfAZQ07+SoA6nrdD4qd4b+f92oa6NLOK2\nlOJcx7ep4j/FbQTeAZxtOggQGPlneWTk/0jjuUxqtMB0DJHIUc3zPj/IudTxXav4y+gCDCTwDiCO\nqkf0oT3z+Yap22h19rGQ7qMq96T8j1+mLoJsKpyTFJEaKn8dx3K9nTPE0V2r+Cs1kcC5AD2Mppi0\nYr3R/Q+sV2oisoo5SRGphlygqPpP71ywhpaFNfifogoq/ip1AS4n8A4gmtLvAJp3zuP8kQcM5XJH\nQeb99EsotfjcZsB7K0yIhJcD1Oj3qF3RJloXbXZs9yr+GrmbwDuAcwC4bEKO0TShdnHCJqLK++n8\n2v0sIrZbuOdCx7YVHusZeEqXE18DuOiGn5gOEzLRFLMg44XyH9wCdCDwEYiIVJ8feJcaTfOEgkb8\nQZjQ9Glm/c7d1UDdcHHCJgo73V/5k7LdySISUZZivPQhotbjNyc6tpDrn3+K3mM+Dtk+rvWdGbJt\nlxbvK+BwxweJ9lXjn6cVcEbII4lEhnkEXfq+9MDvpS696CEJDY9wz38nkXFW2U/fmy7dTaMvAydi\nrL4+E3zV/3e76vI51N9S9oIoT+zrxdaC8pdSeCS3b7W3D3BnykcAPNzk3Rq9jvZAMtCkZi8TscoS\nIMiPBHs1/pSlcb0AFb/nxMTlk5Key8Mbbiu575pzZhBVUEx0QeCD0oKEGL66PpMvb+xarW1WVPyV\nOVRU9fV8b9s9mFmHO5HddjJJ0UGsB+IDLsLLi5+KmLEe2Igj0zsq/jDx9ya30S9hIWfHf17lc5fe\n2511I9pRFHfqKphtZ22m7z1LQxXRWQ2AFkAiegcgdssF9gNrnNukij+MJPoOEx+Vx/2pDzIh5YlK\nn3u8QR3WX92G9IU7ic/JAyA6v5jYo4VuRHWOj8DxYk0IZv07kfD0HoERvsPnu6j4w9iQem8zOHEO\nPeJXcF7dZabjuCsRaFPqditTQURCYAuwE9gbms2r+CNAUtQhkqIOcmPDJ/l1oz+YjmPG6ecBJAJZ\nJoKIBGktgbn8EFLxR6DLEt5jZP3XuK7hi6ajeE/pI1kbErh+jogX5AKL3dmVij+CtYndBMCmdu0N\nJ/GoWL4/31yXBxBT1hCY1snHtROynCx+LdngMdkFbQHwrS3m0Sa30ig6hyvqvU1SdM0O54xYBXy/\nNPTsUvc3BZqVuu3sFTFFAj93u4GqD9LzPBW/h926+1EA2sZu5Ef13mRy0zsNJ/Kw7058nbSh1Pfn\no3WFpPYOA8sJHKXjrYvi1ZqmesJMr/gl3Jn6CFcnzTAdJTyVvrRCIwJv001f5KxZ1U/xjJ2VPPY1\nkFeLbbYhcPZ3eaIIvJtzWg7Vu6jQRgJLKHuA5viFs+I+Z2rzsXSL+8p0lPCVSGAUZ/YCZ1C/ksfO\nwdwZ0R9T9gJzB13O4CM0H+YfJuyuK6HilxJnx63kwcb3M6Te7KqfLJGlZxCvTQLigT2l7vMTmNIQ\nT1LxSxk/b/gUT6bdaDqGhIu6BI6QcnsEL7XmZPFrPf4I8dT+n+NbW8z8I5eSU5hqOo543TFU+hZT\n8UeYH2x7j4u2fsjPdj5jOoqIeJSmeiJcm9hNvNjsupLb/RI+MphGRGpLJ3BJtWUXtOXCrf8ruX1p\nwnwA5mcMMBVJRAzTVI9l3j96Ge8fvQzf2mLu3v1Hluf1qPpFIhJRVPwWezj3bnpvXsRbh4aYjiIi\nLlLxWy6fOG787p+mY4iIi1T8wl0pj5iOICIuUvGLiFhGxS8iYhkVv4iIZVT8IiKWUfGLiFhGxS9M\nO/Rj0xFExEUqfst1qrOWj1v1NR1DRFyk4rfY8KRprGnbxXQMEXGZFmmzRPvY9Sxv3ZP60YdMRxGR\nGjroS6LIF+3Y9jTit8SGgg40WH+Au3b/iWXHgrlmn4i4ZVXMGdzV4M80aHGQ5XWc+73ViN8yj+Te\nRfs6Gzm37memo4hIBYqIopAYzkxbFZLta8RvoRt2Pc0je39pOoaIVGB46uvEpx8P2fZV/Ja6a8/D\nPLnvBtMxROQEP/BSwlh86X5m1h0a0n1pqsdiN373JMf8dbkj5e+mo4hYbWVsdy5u/CEHohq6sj9d\nc9d6fp5oegs3JWtNfhFTYlvkU+iLrfbzg73mrqZ6rOejyO/cYWIiUnM1KX0nqPhFRCyj4hcRMej5\nhHGu71PFLyJi0H0NHnJ9nyp+y8X7jnFG3FemY4iIi3Q4p+U2tm1P89idpmOIiIs04rdUgu8IC1r2\nV+mLGPTPxF+wK7qZ6/vViN8ik5tM5If1ZgEQ7SuidewWw4lExAQVvyVuT/4rd6T8zXQMESnlMwdX\n3KwJnblrgclNJqr0RTzIl167GtSZu1Kl9JjtpiOIiIeo+EVELKM5/ggzKHE2AO3rbODRprcbTiMi\nFUltlmNs3yr+CPJYkwnckvKE6Rgi4nGa6okQtyf/VaUvItWio3oihL+T/g8XCSepzXLIjU6t1WuD\nParHWPGLiIgZGiaKiFhGxS8iYhkVv4iIZVT8IiKWUfGLiFhGxS8iYhkVv4iIZVT8IiKWUfGLiFhG\nxS8iYhkVv4iIZVT8IiKWUfGLiFhGxS8iYhkVv4iIZVT8IiKWUfGLiFhGxS8iYhkVv4iIZVT8IiKW\n+X+ON1tZfj6oqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd2b9dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAAEACAYAAADrz1BBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd149250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEACAYAAAD2sW7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmABwAiBBQAjBBYAjBBYADBCYAHAyP8Bv9yTTrm87roAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd39d4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3efd182710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEACAYAAAD2sW7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f39636750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.imshow(np.array(t['LabelMap']))\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
